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Morimoto J.,ATR Computational Neuroscience Labs | Kawato M.,ATR Brain Information Communication Research Laboratory Group
Journal of the Royal Society Interface | Year: 2015

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the 'understanding the brain by creating the brain' approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain-machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

News Article | December 15, 2016
Site: www.eurekalert.org

A new technique of analyzing brain patterns appears to help people overcome fear and build self-confidence. The approach, developed by a UCLA-led team of neuroscientists, is described in two new papers, published in the journals Nature Communications and Nature Human Behaviour. Their method could have implications for treating people with depression, dementia and anxiety disorders, including post-traumatic stress disorder, said Hakwan Lau, a UCLA associate professor of psychology and the senior author of both studies. It could also play a role in improving leadership training for executives and managers. In the Nature Human Behaviour study, the researchers showed that they could reduce the brain's manifestation of fear using a procedure called decoded neurofeedback, which involves identifying complex patterns of brain activity linked to a specific memory, and then giving feedback to the subject -- for example, in the form of a reward -- based on their brain activity. The researchers tested the technique on 17 undergraduate and graduate students in Japan. Participants were seated in a functional magnetic resonance imaging, or fMRI, scanner and shown patterns of vertical lines in four colors -- red, green, blue and yellow. The blue and yellow images were always shown without shocks, but the red and green patterns were often accompanied by a small electrical shock administered to their feet. As a result, the subjects' brain patterns began to register fear for the red and green images. But the scientists learned that they could use decoded neurofeedback to lessen the subjects' fear of the red pattern. They did this by giving the subjects a small cash reward -- the equivalent of about 10 cents -- each time they spontaneously thought about the red lines (but gave no rewards for thinking about the green lines), which the scientists could determine in real time based on their brain activity. The following day, researchers tested whether the participants still had a fear response to the vertical lines. The red pattern, which had been frightening because it was paired with shocks, became less so because it now was paired with a positive outcome. With the reward as part of the equation, researchers found that participants perspired much less than when they had seen the red lines previously, and their brain's fear signal, centered in the amygdala, was significantly reduced. "After just three days of training, we saw a significant reduction of fear," Lau said. "We changed the association of the 'fear object' from negative to positive." Participants were not told what they had to do to earn the money -- only that the reward was based on their brain activity and that they should try to earn as much money as possible. And each time participants were told they had won money, their brains demonstrated more of the same pattern that had just won them the cash reward. Although participants tried to guess which of their thoughts were triggering the rewards -- some guessed humming music or thinking about a girlfriend, for example -- none actually figured out how they earned the money or recognized that the researchers had effectively reduced their fear of the red lines. "Their brain activity was completely unconscious," Lau said. "That makes sense; a lot of our brain activity is unconscious." Participants did still register fear on their fMRI scans when they saw the green pattern because, without the financial rewards, they still primarily associated the color with shocks. The findings could help improve upon standard behavioral therapy, in which a person who is afraid of a certain object is exposed to photos of that object, or even the object itself -- which can be frightening enough that many people cannot complete treatment. Lau said using "unconscious fear reduction," like in the experiment, could be more effective in many cases. In the Nature Communications study, which was published today, Lau and his colleagues used decoded neurofeedback to increase people's confidence levels. Ten participants were seated in an fMRI scanner and asked to watch a computer screen with hundreds of dots moving in different directions. Participants were asked whether the majority of dots were moving to the left or the right, and how confident they were in their responses. That initial feedback gave the researchers a chance to see how high confidence and low confidence were represented in brain patterns. Participants then were shown dots moving in random motion and told to think about anything -- and that certain thoughts would earn them cash rewards. Every time the brain pattern looked like it was representing high confidence, the participant received a reward of up to the equivalent of 10 cents; subjects received smaller rewards if their brain activity indicated less confidence. Next, the researchers showed the students the images from the first phase of the experiment -- with numerous dots primarily moving in one direction or the other. The scientists found that although students weren't any better at guessing the primary direction of the dots' motion, they had become more confident in their guesses. By studying brain patterns, Lau said, neuroscientists can decode people's thoughts about food, love, money and many other concepts, which eventually could help them design treatments for eating disorders, gambling addiction and more. The researchers next will determine whether the techniques described in the papers can be used to help patients with real phobias. "We are cautiously optimistic," he said. Co-authors of both studies include Mitsuo Kawato, professor and director of the ATR Brain Information Communication Research Laboratory Group in Kyoto, Japan; Ai Koizumi, a UCLA postdoctoral scholar; Ben Seymour, a neuroscientist at the University of Cambridge; and Aurelio Cortese, a doctoral student in Kawato's laboratory. Lau's research is funded mainly by the National Institutes of Health's National Institute of Neurological Disorders and Stroke (grant R01NS088628).

Binder A.,TU Berlin | Binder A.,Fraunhofer Institute FIRST | Samek W.,TU Berlin | Samek W.,Fraunhofer Institute FIRST | And 4 more authors.
Computer Vision and Image Understanding | Year: 2013

In this paper we propose a novel biased random sampling strategy for image representation in Bag-of-Words models. We evaluate its impact on the feature properties and the ranking quality for a set of semantic concepts and show that it improves performance of classifiers in image annotation tasks and increases the correlation between kernels and labels. As second contribution we propose a method called Output Kernel Multi-Task Learning (MTL) to improve ranking performance by transfer information between classes. The main advantages of output kernel MTL are that it permits asymmetric information transfer between tasks and scales to training sets of several thousand images. We give a theoretical interpretation of the method and show that the learned contributions of source tasks to target tasks are semantically consistent. Both strategies are evaluated on the ImageCLEF PhotoAnnotation dataset. Our best visual result which used the MTL method was ranked first according to mean Average Precision (mAP) within the purely visual submissions in the ImageCLEF 2011 PhotoAnnotation Challenge. Our multi-modal submission achieved the first rank by mAP among all submissions in the same competition. © 2012 Elsevier B.V. All rights reserved.

Hagura N.,ATR Brain Information Communication Research Laboratory Group | Hagura N.,University College London | Hirose S.,ATR Brain Information Communication Research Laboratory Group | Matsumura M.,Kyoto University | And 2 more authors.
Proceedings of the Royal Society B: Biological Sciences | Year: 2012

When confronted with complex visual scenes in daily life, how do we know which visual information represents our own hand? We investigated the cues used to assign visual information to one's own hand. Wrist tendon vibration elicits an illusory sensation of wrist movement. The intensity of this illusion attenuates when the actual motionless hand is visually presented. Testing what kind of visual stimuli attenuate this illusion will elucidate factors contributing to visual detection of one's own hand. The illusion was reduced when a stationary objectwas shown, but only when participants knew it was controllable with their hands. In contrast, the visual image of their own hand attenuated the illusion even when participants knew that it was not controllable. We suggest that long-termknowledge about the appearance of the body and short-termknowledge about controllability of a visual object are combined to robustly extract our own body from a visual scene. © 2012 The Royal Society.

Hagura N.,University College London | Hagura N.,ATR Brain Information Communication Research Laboratory Group | Kanai R.,University College London | Orgs G.,University College London | Haggard P.,University College London
Proceedings of the Royal Society B: Biological Sciences | Year: 2012

Professional ball game players report the feeling of the ball 'slowing-down' before hitting it. Because effective motor preparation is critical in achieving such expert motor performance, these anecdotal comments imply that the subjective passage of time may be influenced by preparation for action. Previous reports of temporal illusions associated with action generally emphasize compensation for suppressed sensory signals that accompany motor commands. Here, we show that the time is perceived slowed-down during preparation of a ballistic reaching movement before action, involving enhancement of sensory processing. Preparing for a reaching movement increased perceived duration of a visual stimulus. This effect was tightly linked to action preparation, because the amount of temporal dilation increased with the information about the upcoming movement. Furthermore, we showed a reduction of perceived frequency for flickering stimuli and an enhanced detection of rapidly presented letters during action preparation, suggesting increased temporal resolution of visual perception during action preparation. We propose that the temporal dilation during action preparation reflects the function of the brain to maximize the capacity of sensory information-acquisition prior to execution of a ballistic movement. This strategy might facilitate changing or inhibiting the planned action in response to last-minute changes in the external environment. © 2012 The Royal Society.

PubMed | Japan Society for the Promotion of Science, Kyoto University and ATR Brain Information Communication Research Laboratory Group
Type: | Journal: NeuroImage | Year: 2016

Transcranial direct current stimulation (tDCS) can modulate mind wandering, which is a shift in the contents of thought away from an ongoing task and/or from events in the external environment to self-generated thoughts and feelings. Although modulation of the mind-wandering propensity is thought to be associated with neural alterations of the lateral prefrontal cortex (LPFC) and regions in the default mode network (DMN), the precise neural mechanisms remain unknown. Using functional magnetic resonance imaging (fMRI), we investigated the causal relationships among tDCS (one electrode placed over the right IPL, which is a core region of the DMN, and another placed over the left LPFC), stimulation-induced directed connection alterations within the DMN, and modulation of the mind-wandering propensity. At the behavioral level, anodal tDCS on the right IPL (with cathodal tDCS on the left LPFC) reduced mind wandering compared to the reversed stimulation. At the neural level, the anodal tDCS on the right IPL decreased the afferent connections of the posterior cingulate cortex (PCC) from the right IPL and the medial prefrontal cortex (mPFC). Furthermore, mediation analysis revealed that the changes in the connections from the right IPL and mPFC correlated with the facilitation and inhibition of mind wandering, respectively. These effects are the result of the heterogeneous function of effective connectivity: the connection from the right IPL to the PCC inhibits mind wandering, whereas the connection from the mPFC to the PCC facilitates mind wandering. The present study is the first to demonstrate the neural mechanisms underlying tDCS modulation of mind-wandering propensity.

PubMed | University of Tokyo and ATR Brain Information Communication Research Laboratory Group
Type: | Journal: Psychiatry and clinical neurosciences | Year: 2016

Psychiatry research has long experienced a stagnation stemming from a lack of understanding of the neurobiological underpinnings of phenomenologically defined mental disorders. Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders, thereby recasting current nosology in more biologically meaningful dimensions. In this review, we highlight recent investigations in computational neuroscience that undertook either theory- or data-driven approaches to quantitatively delineate the mechanisms of mental disorders. The theory-driven approach, including reinforcement learning models, plays an integrative role in this process by enabling correspondence between behavior and disorder-specific alterations at multiple levels of brain organization, ranging from molecules to cells to circuits. Previous studies explicated a plethora of defining symptoms of mental disorders, including anhedonia, inattention, and poor executive function. The data-driven approach, on the other hand, is an emerging field in computational neuroscience seeking to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorder-specific features were used for automatic case-control classification. For many disorders, the reported accuracies have reached 90% or more. However, we note that rigorous tests on independent cohorts are critically required to translate this research into clinical applications. Finally, we discuss the utility of the disorder-specific features found by the data-driven approach to psychiatric therapies, including neurofeedback. Such developments will allow simultaneous diagnosis and treatment of mental disorders using neuroimaging, thereby establishing theranostics for the first time in clinical psychiatry.

Kawato M.,ATR Brain Information Communication Research Laboratory Group
2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 | Year: 2013

Japanese MEXT started SRPBS (strategic research for promotion of brain sciences) in 2008. Field A was on BMI and I am the leader of this. I will describe achievement within this large group funding. Within ATR, we have developed next generation noninvasive decoding method as well as decoded neurofeedback method. © 2013 IEEE.

Kawato M.,ATR Brain Information Communication Research Laboratory Group
2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 | Year: 2014

One of the most important assumptions in neuroscience and brain science is that neural activity in the brain is the cause of our mind including consciousness. Most studies of human learning/memory/cognition have concentrated on examining correlations between behavioral and neural activity changes rather than establishing cause-and-effect relationships. Even for animal studies, the most frequently used technique is examining temporal correlation between neural activities and some hypothetical computational variables proposed by experimenters. The lack of experimental tools examining cause and effect relationships in the systems neuroscience severely constrains its progress and applicability to practical problems. To fill this gap between major concepts and current technology, by applying a novel online-feedback method utilizing decoded functional magnetic resonance imaging (fMRI) signals, we developed a new technique to manipulate neural codes [1], DecNef. © 2014 IEEE.

Ogasawara H.,Japan National Institute of Information and Communications Technology | Ogasawara H.,ATR Brain Information Communication Research Laboratory Group | Ogasawara H.,Astellas Pharma Inc. | Kawato M.,ATR Brain Information Communication Research Laboratory Group
BMC Systems Biology | Year: 2010

Background: Protein kinase Mζ (PKMζ), the brain-specific, atypical protein kinase C isoform, plays a key role in long-term maintenance of memory. This molecule is essential for long-term potentiation of the neuron and various modalities of learning such as spatial memory and fear conditioning. It is unknown, however, how PKMζ stores information for long periods of time despite molecular turnover.Results: We hypothesized that PKMζ forms a bistable switch because it appears to constitute a positive feedback loop (PKMζ induces its local synthesis) part of which is ultrasensitive (PKMζ stimulates its synthesis through dual pathways). To examine this hypothesis, we modeled the biochemical network of PKMζ with realistic kinetic parameters. Bifurcation analyses of the model showed that the system maintains either the up state or the down state according to previous inputs. Furthermore, the model was able to reproduce a variety of previous experimental results regarding synaptic plasticity and learning, which suggested that it captures the essential mechanism for neuronal memory. We proposed in vitro and in vivo experiments that would critically examine the validity of the model and illuminate the pivotal role of PKMζ in synaptic plasticity and learning.Conclusions: This study revealed bistability of the PKMζ network and supported its pivotal role in long-term storage of memory. © 2010 Ogasawara and Kawato; licensee BioMed Central Ltd.

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